sep_smooths_year2
Load all required libraries.
library(tidyverse)## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.3 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.0 v forcats 0.5.1## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(broom)Read in raw data from RDS.
raw_data <- readRDS("./year2.RDS")Make a few small modifications to names and data for visualizations.
final_data <- raw_data %>% mutate(log_copy_per_L = log10(mean_copy_num_L)) %>%
rename(Facility = wrf) %>%
mutate(Facility = recode(Facility,
"NO" = "WRF A",
"MI" = "WRF B",
"CC" = "WRF C"))Seperate the data by gene target to ease layering in the final plot
#make three data layers
only_positives <<- subset(final_data, (!is.na(final_data$Facility)))
only_n1 <- subset(only_positives, target == "N1")
only_n2 <- subset(only_positives, target == "N2")
only_background <<-final_data %>%
select(c(date, cases_cum_clarke, new_cases_clarke, X7_day_ave_clarke)) %>%
group_by(date) %>% summarise_if(is.numeric, mean)
#specify fun colors
background_color <- "#7570B3"
seven_day_ave_color <- "#E6AB02"
marker_colors <- c("N1" = '#1B9E77',"N2" ='#D95F02')#remove facilty C for now
#only_n1 <- only_n1[!(only_n1$Facility == "WRF C"),]
#only_n2 <- only_n2[!(only_n2$Facility == "WRF C"),]
only_n1 <- only_n1[!(only_n1$Facility == "WRF A" & only_n1$date == "2020-11-02"), ]
only_n2 <- only_n2[!(only_n2$Facility == "WRF A" & only_n2$date == "2020-11-02"), ]Build the main plot
#first layer is the background epidemic curve
p1 <- only_background %>%
plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~new_cases_clarke,
type = "bar",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Daily Cases: ', new_cases_clarke),
alpha = 0.5,
name = "Daily Reported Cases",
color = background_color,
colors = background_color,
showlegend = FALSE) %>%
layout(yaxis = list(title = "Clarke County Daily Cases", showline=TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#renders the main plot layer two as seven day moving average
p1 <- p1 %>% plotly::add_trace(x = ~date, y = ~X7_day_ave_clarke,
type = "scatter",
mode = "lines",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Seven-Day Moving Average: ', X7_day_ave_clarke),
name = "Seven Day Moving Average Athens",
line = list(color = seven_day_ave_color),
showlegend = FALSE)
#renders the main plot layer three as positive target hits
p2 <- plotly::plot_ly() %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n1,
symbol = ~Facility,
marker = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
plotly::add_trace(x = ~date, y = ~mean_copy_num_L,
type = "scatter",
mode = "markers",
hoverinfo = "text",
text = ~paste('</br> Date: ', date,
'</br> Facility: ', Facility,
'</br> Target: ', target,
'</br> Copies/L: ', round(mean_copy_num_L, digits = 2)),
data = only_n2,
symbol = ~Facility,
marker = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(yaxis = list(title = "SARS CoV-2 Copies/L",
showline = TRUE,
type = "log",
dtick = 1,
automargin = TRUE)) %>%
layout(legend = list(orientation = "h", x = 0.2, y = -0.3))
#adds the limit of detection dashed line
p2 <- p2 %>% plotly::add_segments(x = as.Date("2021-06-30"),
xend = ~max(date + 10),
y = 3571.429, yend = 3571.429,
opacity = 0.35,
line = list(color = "black", dash = "dash")) %>%
layout(annotations = list(x = as.Date("2021-06-30"), y = 3.8, xref = "x", yref = "y",
text = "Limit of Detection", showarrow = FALSE))
p1 p2Combine the two main plot pieces as a subplot
#seperate n1 and n2 frames by site
#n1
wrf_a_only_n1 <- subset(only_n1, Facility == "WRF A")
wrf_b_only_n1 <- subset(only_n1, Facility == "WRF B")
wrf_c_only_n1 <- subset(only_n1, Facility == "WRF C")
#n2
wrf_a_only_n2 <- subset(only_n2, Facility == "WRF A")
wrf_b_only_n2 <- subset(only_n2, Facility == "WRF B")
wrf_c_only_n2 <- subset(only_n2, Facility == "WRF C")
#rejoin the old data frames then seperate in to averages for each plant.
wrfa_both <- full_join(wrf_a_only_n1, wrf_a_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfb_both <- full_join(wrf_b_only_n1, wrf_b_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")wrfc_both <- full_join(wrf_c_only_n1, wrf_c_only_n2)%>%
select(c(date, mean_total_copies)) %>%
group_by(date) %>%
summarize_if(is.numeric, mean) %>%
ungroup() %>%
mutate(log_total_copies_both = log10(mean_total_copies))## Joining, by = c("date", "new_cases_clarke", "cases_cum_clarke", "X7_day_ave_clarke", "Facility", "collection_num", "target", "mean_copy_num_uL_rxn", "mean_copy_num_L", "sd_L", "mean_total_copies", "sd_total_copies", "log_copy_per_L")#get max date
maxdate <- max(wrfa_both$date)
mindate <- min(wrfa_both$date)Build loess smoothing figures figures
This makes the individual plots
#**************************************WRF A PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_botha <- ggplot(wrfa_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_botha<<-..y..), method = "loess", color = '#1B9E77',
span = 0.6, n = 78)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_botha## `geom_smooth()` using formula 'y ~ x'
fit_botha## [1] 11.54764 11.58211 11.61657 11.65103 11.68551 11.72003 11.75460 11.78916
## [9] 11.82363 11.85807 11.89251 11.92700 11.96157 11.99671 12.03266 12.06909
## [17] 12.10566 12.14205 12.17790 12.21291 12.25087 12.28863 12.32217 12.35476
## [25] 12.38646 12.41734 12.44746 12.47689 12.50570 12.53465 12.56395 12.59279
## [33] 12.62037 12.64590 12.66743 12.68477 12.69970 12.71402 12.72954 12.74804
## [41] 12.77132 12.79997 12.83230 12.86676 12.90176 12.93575 12.96714 12.99438
## [49] 13.02383 13.05282 13.07970 13.11116 13.14452 13.17713 13.20632 13.22941
## [57] 13.24375 13.24881 13.24741 13.24189 13.23463 13.22798 13.22053 13.20954
## [65] 13.19533 13.17822 13.15853 13.13660 13.11275 13.08730 13.05936 13.02968
## [73] 12.99775 12.96356 12.92711 12.88839 12.84740 12.80414#assign fits to a vector
both_trenda <- fit_botha
#extract y min and max for each
limits_botha <- ggplot_build(extract_botha)$data## `geom_smooth()` using formula 'y ~ x'limits_botha <- as.data.frame(limits_botha)
both_ymina <- limits_botha$ymin
both_ymaxa <- limits_botha$ymax
#reassign dataframes (just to be safe)
work_botha <- wrfa_both
#fill in missing dates to smooth fits
work_botha <- work_botha %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_botha <- work_botha$date
#create a new smooth dataframe to layer
smooth_frame_botha <- data.frame(date_vec_botha, both_trenda, both_ymina, both_ymaxa)#WRF A
#plot smooth frames
p_wrf_a <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_botha, y = ~both_trenda,
data = smooth_frame_botha,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha,
'</br> Median Log Copies: ', round(both_trenda, digits = 2)),
line = list(color = '#1B9E77', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_botha, ymin = ~both_ymina, ymax = ~both_ymaxa,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_botha, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxa, digits = 2),
'</br> Min Log Copies: ', round(both_ymina, digits = 2)),
name = "",
fillcolor = '#1B9E77',
line = list(color = '#1B9E77')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF A") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfa_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#1B9E77', size = 6, opacity = 0.65))
p_wrf_asave(p_wrf_a, file = "./site_objects/wrf_a_year2.rda")#**************************************WRF B PLOT**********************************************
#add trendlines
#extract data from geom_smooth
#both extract
# *********************************span 0.6***********************************
#*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothb <- ggplot(wrfb_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothb<<-..y..), method = "loess", color = '#D95F02',
span = 0.6, n = 78)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothb## `geom_smooth()` using formula 'y ~ x'
fit_bothb## [1] 10.71498 10.80514 10.89353 10.98023 11.06530 11.14881 11.23081 11.31146
## [9] 11.39074 11.46852 11.54463 11.61894 11.69128 11.76189 11.83102 11.89853
## [17] 11.96430 12.02821 12.09013 12.14992 12.20679 12.26188 12.31991 12.38206
## [25] 12.44593 12.50911 12.56921 12.62381 12.67052 12.71180 12.75110 12.78746
## [33] 12.81995 12.84760 12.86477 12.86924 12.86522 12.85694 12.84859 12.84439
## [41] 12.84856 12.85854 12.86884 12.87930 12.88973 12.89997 12.90984 12.91918
## [49] 12.93625 12.95367 12.96501 12.97870 12.99335 13.00756 13.01993 13.02907
## [57] 13.03357 13.03429 13.03307 13.03001 13.02521 13.01877 13.01055 13.00038
## [65] 12.98831 12.97438 12.95866 12.94120 12.92205 12.90127 12.87867 12.85447
## [73] 12.82854 12.80089 12.77151 12.74039 12.70755 12.67297#assign fits to a vector
both_trendb <- fit_bothb
#extract y min and max for each
limits_bothb <- ggplot_build(extract_bothb)$data## `geom_smooth()` using formula 'y ~ x'limits_bothb <- as.data.frame(limits_bothb)
both_yminb <- limits_bothb$ymin
both_ymaxb <- limits_bothb$ymax
#reassign dataframes (just to be safe)
work_bothb <- wrfb_both
#fill in missing dates to smooth fits
work_bothb <- work_bothb %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothb <- work_bothb$date
#create a new smooth dataframe to layer
smooth_frame_bothb <- data.frame(date_vec_bothb, both_trendb, both_yminb, both_ymaxb)#WRF B
#plot smooth frames
p_wrf_b <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothb, y = ~both_trendb,
data = smooth_frame_bothb,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb,
'</br> Median Log Copies: ', round(both_trendb, digits = 2)),
line = list(color = '#D95F02', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothb, ymin = ~both_yminb, ymax = ~both_ymaxb,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothb, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxb, digits = 2),
'</br> Min Log Copies: ', round(both_yminb, digits = 2)),
name = "",
fillcolor = '#D95F02',
line = list(color = '#D95F02')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF B") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfb_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#D95F02', size = 6, opacity = 0.65))
p_wrf_bsave(p_wrf_b, file = "./site_objects/wrf_b_year2.rda")#**************************************WRF C PLOT********************************************** #add trendlines #extract data from geom_smooth # *********************************span 0.6*********************************** #*****************Must always update the n = TOTAL NUMBER OF DAYS*************************
extract_bothc <- ggplot(wrfc_both, aes(x = date, y = log_total_copies_both)) +
stat_smooth(aes(outfit=fit_bothc<<-..y..), method = "loess", color = '#E7298A',
span = 0.6, n = 78)## Warning: Ignoring unknown aesthetics: outfit#look at the fits to align dates and total observations
#both
extract_bothc## `geom_smooth()` using formula 'y ~ x'
fit_bothc## [1] 10.67242 10.76932 10.86302 10.95329 11.03992 11.12271 11.20144 11.27615
## [9] 11.34713 11.41456 11.47860 11.53943 11.59721 11.64950 11.69477 11.73459
## [17] 11.77053 11.80415 11.83701 11.87069 11.88787 11.90247 11.93127 11.95898
## [25] 11.98599 12.01268 12.03942 12.06661 12.09462 12.12056 12.14249 12.16225
## [33] 12.18165 12.20253 12.22873 12.26126 12.29762 12.33534 12.37194 12.40492
## [41] 12.43183 12.45193 12.46718 12.47903 12.48896 12.49842 12.50890 12.52185
## [49] 12.52542 12.52800 12.54168 12.55695 12.57321 12.58989 12.60640 12.62216
## [57] 12.63658 12.65330 12.67371 12.69392 12.71001 12.71808 12.72009 12.72043
## [65] 12.71888 12.71521 12.70918 12.70057 12.68914 12.67467 12.65775 12.63799
## [73] 12.61572 12.59096 12.56369 12.53391 12.50164 12.46687#assign fits to a vector
both_trendc <- fit_bothc
#extract y min and max for each
limits_bothc <- ggplot_build(extract_bothc)$data## `geom_smooth()` using formula 'y ~ x'limits_bothc <- as.data.frame(limits_bothc)
both_yminc <- limits_bothc$ymin
both_ymaxc <- limits_bothc$ymax
#reassign dataframes (just to be safe)
work_bothc <- wrfc_both
#fill in missing dates to smooth fits
work_bothc <- work_bothc %>% complete(date = seq(min(date), max(date), by = "1 day"))
date_vec_bothc <- work_bothc$date
#create a new smooth dataframe to layer
smooth_frame_bothc <- data.frame(date_vec_bothc, both_trendc, both_yminc, both_ymaxc)#WRF C
#plot smooth frames
p_wrf_c <- plotly::plot_ly() %>%
plotly::add_lines(x = ~date_vec_bothc, y = ~both_trendc,
data = smooth_frame_bothc,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc,
'</br> Median Log Copies: ', round(both_trendc, digits = 2)),
line = list(color = '#E7298A', size = 8, opacity = 0.65),
showlegend = FALSE) %>%
layout(xaxis = list(range = c(mindate - 7, maxdate + 7))) %>% #buffer here
plotly::add_ribbons(x ~date_vec_bothc, ymin = ~both_yminc, ymax = ~both_ymaxc,
showlegend = FALSE,
opacity = 0.25,
hoverinfo = "text",
text = ~paste('</br> Date: ', date_vec_bothc, #leaving in case we want to change
'</br> Max Log Copies: ', round(both_ymaxc, digits = 2),
'</br> Min Log Copies: ', round(both_yminc, digits = 2)),
name = "",
fillcolor = '#E7298A',
line = list(color = '#E7298A')) %>%
layout(yaxis = list(title = "Total Log10 SARS CoV-2 Copies",
showline = TRUE,
automargin = TRUE)) %>%
layout(xaxis = list(title = "Date")) %>%
layout(title = "WRF C") %>%
plotly::add_markers(x = ~date, y = ~log_total_copies_both,
data = wrfc_both,
hoverinfo = "text",
showlegend = FALSE,
text = ~paste('</br> Date: ', date,
'</br> Actual Log Copies: ', round(log_total_copies_both, digits = 2)),
marker = list(color = '#E7298A', size = 6, opacity = 0.65))
p_wrf_csave(p_wrf_c, file = "./site_objects/wrf_c_year2.rda")keeping in case
#save(wrfa_both, file = "./plotly_objs/wrfa_both.rda")
#save(wrfb_both, file = "./plotly_objs/wrfb_both.rda")
#save(wrfc_both, file = "./plotly_objs/wrfc_both.rda")#save(date_vec_botha, file = "./plotly_objs/date_vec_botha.rda")
#save(date_vec_bothb, file = "./plotly_objs/date_vec_bothb.rda")
#save(date_vec_bothc, file = "./plotly_objs/date_vec_bothc.rda")#save(both_ymina, file = "./plotly_objs/both_ymina.rda")
#save(both_ymaxa, file = "./plotly_objs/both_ymaxa.rda")
#save(both_yminb, file = "./plotly_objs/both_yminb.rda")
#save(both_ymaxb, file = "./plotly_objs/both_ymaxb.rda")
#save(both_yminc, file = "./plotly_objs/both_yminc.rda")
#save(both_ymaxc, file = "./plotly_objs/both_ymaxc.rda")